Constrained image restoration with a multinomial prior

Abstract

A Bayesian image processing model is proposed based on, a Markovian multinomial prior. The technique has application in texture segmentation where its introduction of spatial context can improve segmentation accuracy by 60%. Other applications include general image restoration where 18 dB SNR improvement is possible. In addition, the computational complexity of the system is low, making it ideal as a component part of other systems. We show quantitative experiments to illustrate the performance of the algorithm, and groundtruth examples are provided to show the effect in practice.